A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks

This source preferred by Marcin Budka

Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.

http://eprints.bournemouth.ac.uk/20437/

http://www.scopus.com/inward/record.url?eid=2-s2.0-84874238598&partnerID=40&md5=3b04d0bc1d493643106baa8d17d94d33

Pages: 996-1001

DOI: 10.1109/ASONAM.2012.173

This data was imported from DBLP:

Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.

http://eprints.bournemouth.ac.uk/20437/

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6423126

Journal: ASONAM

Pages: 996-1001

Publisher: IEEE Computer Society

ISBN: 978-0-7695-4799-2

DOI: 10.1109/ASONAM.2012.173

This data was imported from Scopus:

Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.

http://eprints.bournemouth.ac.uk/20437/

Journal: Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Pages: 996-1001

ISBN: 9780769547992

DOI: 10.1109/ASONAM.2012.173

We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server. © 2012 IEEE.

This data was imported from Web of Science (Lite):

Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K., Budka, M. and IEEE

http://eprints.bournemouth.ac.uk/20437/

Journal: 2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM)

Pages: 996-1001

ISBN: 978-1-4673-2497-7

DOI: 10.1109/ASONAM.2012.173

The data on this page was last updated at 17:31 on November 21, 2017.